A Kalman Filter Based PID Controller for Stochastic Systems

摘要：

This paper proposes a neural network PID controller for stochastic systems with unknown parameters. The controller minimizes the innovation dual control objective function, which combines the estimation objective and control objective to a mixed problem. The system parameters and the covariance matrix needed to update the neural network are estimated by the standard Kalman filter. The gradient descent algorithm is used to train the weights of the neural network. Simulation results show the neural network PID controller has dual property that can achieve preferable estimation performance and satisfactory control performance.

作者: Changyuan Fan Hui Ju Baoqiang Wang

作者单位: Department of Control Engineering Chengdu University Of Information Technology Chengdu, Sichuan Province, China

会议类型: 国际会议

会议名称: 2006 International Conference on Communications,Circuits and Systems(第四届国际通信、电路与系统学术会议)

会议地点: 广西桂林

会议语种:英文

页码: 2055-2057

在线出版日期: 2006-06-25（万方平台首次上网日期，不代表论文的发表时间）